What Underpins Model Credibility? Three Philosophical Perspectives
from In Silico Trials, Real Impacts! Hosted by UK CEiRSI | InSilicoUK
How do philosophers of science think about model evaluation? Dive deep into the crucial process of model evaluation, a fundamental step in digital science that determines the effectiveness and precision of computational models. This episode explores three primary perspectives on model quality: the mirror view, relevant similarity, and fitness for purpose. Learn about the complexities and challenges involved in evaluating models, from data limitations to the intricate balance between accuracy and usability. Whether you're a researcher, regulator, or a curious listener, discover how these cutting-edge methodologies are reshaping healthcare, improving safety, efficiency, and equitability, one simulated trial at a time. Join us as we delve into the philosophy behind model evaluation and unveil the real-world impact of digital innovation. Parker WS. Model Evaluation: An Adequacy-for-Purpose View. Philos Sci. 2020;87(3):457-477. Bokulich A, Parker W. Data models, representation and adequacy-for-purpose. Eur J Philos Sci. 2021;11(31).
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What underpins model credibility? Join us in our next episode as we delve into the complexities of evaluating model quality. Discover how philosophers of science tackle concepts like accurate representations and fit-for-purpose models. Explore the pragmatic-representational view, highlighting context-dependent evaluation and the evolutionary nature of data models. Tune in for a thought-provoking exploration of model evaluation challenges!
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